Recently, machine learning and artificial intelligence have seen a rise in prominence in a variety of different fields and for a number of specific applications, largely due to advances in computing technology that enables the implementation of advanced algorithms and techniques. One such area is information technology (IT) support/automated customer service, where client devices operated by customers communicate with IT systems of, e.g., a company to resolve problems and issues with the company's service. In one example, a customer may utilize his client device (e.g., desktop, laptop, mobile device) to submit an electronic problem ticket, consisting of computer text that describes the problem, to the IT system of a company's customer service center, where the problem ticket is automatically routed to a customer service system and/or representative for action.
Existing computing systems that analyze customer requests for routing to customer service agents or systems can leverage computing techniques such as natural language processing (NLP) and/or machine learning to automatically identify an issue or problem in the customer's submitted ticket and classify the ticket in the proper category—so that the ticket is correctly routed to a system or representative that can best address the problem. However, in order to have success using NLP and/or machine learning techniques in this way, the computing system that routes the electronic problem ticket must have a large corpus of historical data around submitted problem tickets, categorizations of those tickets, and routing decisions—so that the subsequent routing decisions made by the computing system are more accurate. Many routing systems do not have a sufficient amount of historical data for particular segments or business domains, in order to use NLP and machine learning effectively. For a machine learning system, a lot of training data is required—meaning for each ticket category, a machine learning system needs to collect hundreds, and perhaps thousands, of training data elements with a correct category label to be able to train a machine learning classification algorithm to classify a ticket based on the ticket description. But the reality is, most systems do not have enough training data, typically because there is not enough capacity to manually label all the data. Especially for some new business domains, there may be no historical training data.